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Based on the principles of importance sampling and resampling, sequential Monte Carlo (SMC) encompasses a large set of powerful techniques dealing with complex stochastic dynamic systems. Many of these systems possess strong memory, with which future information can help sharpen the inference about the current state. By providing theoretical justification of several existing algorithms and introducing several new ones, we study systematically how to construct efficient SMC algorithms to take advantage of the "future" information without creating a substantially high computational burden. The main idea is to allow for lookahead in the Monte Carlo process so that future information can be utilized in weighting and generating Monte Carlo samples, or resampling from samples of the current state.Comment: Published in at http://dx.doi.org/10.1214/12-STS401 the Statistical Science (http://www.imstat.org/sts/) by the Institute of Mathematical Statistics (http://www.imstat.org

Abstract:To recognize individual activities in multi-resident environments with pervasive sensors, some researchers have pointed out that finding data associations can contribute to activity recognition and previous methods either need or infer data association when recognizing new multi-resident activities based on new observations from sensors. However, it is often difficult to find out data associations, and available approaches to multi-resident activity recognition degrade when the data association is not given or induced with low accuracy. This paper exploits some simple knowledge of multi-resident activities through defining Combined label and the state set, and proposes a two-stage activity recognition method for multi-resident activity recognition. We define Combined label states at the model building phase with the help of data association, and learn Combined label states at the new activity recognition phase without the help of data association. Our two stages method is embodied in the new activity recognition phase, where we figure out multi-resident activities in the second stage after learning Combined label states at first stage. The experiments using the multi-resident CASAS data demonstrate that our method can increase the recognition accuracy by approximately 10%.

In amount of practical applications of wireless power transfer (WPT), charging electric vehicles (EVs) has attracted much attention. Dynamic WPT is considered as a solution to the problem of battery bottleneck and the difficulty of convenient charging encountered in the development of EV. However, the transfer power can hardly be maintained stable under large coil misalignment in movement. What is more, the transfer efficiency will decline correspondingly. A method of optimisation design is presented from the perspective of against misalignment for dynamic series-series (SS) WPT system. The primary compensation capacitance is well designed to regulate transfer power fluctuation in WPT system. Then the optimal load is matched by DC-DC converter to guarantee the highest average transfer efficiency. A secondary-only resonant SS WPT system with fixed frequency is finally built. The power transfer profile is smooth against coupling coefficient to realise high tolerance to position as the power drop (raise) percentage is no more than 10% (20%) within 200% variation of coupling coefficient for different loads. Meanwhile, the efficiency can always be maintained at a high level.

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These results demonstrated that adventitial fibroblasts are activated and can produce cytokines and chemokines before the neointimal hyperplasia. They may exert a potential effect on the development of neointimal hyperplasia in TV.

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